1. Field of the Invention
The present invention relates to imaging systems. More specifically, the present invention relates to spectrally segmented hyper-spectral image processing systems and methods.
2. Description of the Related Art
Conventional hyper-spectral images are constructed by scanning an image field in either time or space to generate information with both spectral and spatial content. The information is then processed and reconstructed into a hyper-spectral cube consisting of spatial and spectral information. A hyper-spectral cube consists of data aligned along three dimensions. Two of the dimensions are the ‘X’ and ‘Y’ coordinates of the image field, and the third dimension is wavelength. In many applications, the wavelengths involved are in the infrared and visible bands.
One application of hyper-spectral image utilization is target discrimination. Target discrimination is useful in military applications. By analyzing data between the various wavelengths, targets can be realized that would not otherwise be discernable from single, or blended, spectrum data. That is, a target can be pulled out of the clutter of the various image spectra.
Hyper-spectral imaging is also useful in medical imaging, agricultural mapping, and other image processing applications.
Hyper-spectral image processing typically involves, as a first step, construction of a hyper-spectral cube of image data. This can be accomplished in a number of ways. A typical image field reflects or emits energy over a continuous band of wavelengths. The continuous band of wavelengths is discriminated into plural narrow bands and mapped into the hyper-spectral cube. One approach to wavelength discrimination is to make multiple exposures of the image field, each sensitive to one of the plural narrow spectral bands. Wavelength discrimination can be achieved using narrow-band filters or diffractive elements that are positioned to limit the wavelengths that reach a focal plane sensing element during each of the multiple exposures. Tunable filters are also used to discriminate the plural wavelengths. However, since each exposure takes a finite period of time to allow energy to integrate on the image focal plane, the exposure period can become prohibitively long. This is particularly true in the case of an image field including moving objects, or where the focal plane sensing element is attached to a moving platform, such as an aircraft or missile.
Another approach to generating a hyper-spectral cube of image data is to use a prism or diffraction grating to spread the continuous band of wavelengths onto a focal plane sensing element during a single exposure. This creates an image that is spectrally blurred across one or more dimensions. Given that the degree of spectral spreading is known from the selection of the diffractive element, image data processing techniques are used to mathematically reconstruct the spectral and spatial information that has been blurred together. Thus, the hyper-spectral cube can be filled with data that is processed from the spectrally blurred image exposure. The scanning and reconstruction processes can be very time consuming. In addition, errors can occur in the scanning process if there is movement in the scene during exposure. Using a higher number of diffractive orders reduces errors, however, this results in greater amounts of data that must be processed during reconstruction. While this approach does allow a single exposure to be made of the image field, the data processing amounts to an inordinately large and time consuming task. For example, in the case where a focal plane array that is 100 pixels by 100 pixels in size is used to generate a hyper-spectral cube that resolves 100 wavelengths, the dimension of the cube are 106 data points. The processing involved in generating the hyper-spectral cube is essentially a matrix inversion, which requires processing the square of the data. Thus, data processing in the order of 1012 is required to generate the hyper-spectral cube. As a practical matter, the prior art hyper-spectral imaging systems must gather the raw data in the field and then process it at a later time. In higher resolution system, such as 1000 by 1000 pixel systems, the processing time can extend into hours or even days of computer processing time. Obviously, in a tactical environment, the passage of time renders the ultimately resolved information far less useful than if it were available in real time.
Thus, there is a need in the art for a system and method of producing hyper-spectral image cube data, for use in hyper-spectral processing, which does not require multiple exposures or inordinately large amounts of processing time.